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Impact of Myocardial Scar on Prognostic Implication of Secondary Mitral Regurgitation in Heart Failure Stage B heart failure: management of asymptomatic left ventricular systolic dysfunction A pragmatic approach to the use of inotropes for the management of acute and advanced heart failure: An expert panel consensus Nuclear Imaging of the Cardiac Sympathetic Nervous System: A Disease-Specific Interpretation in Heart Failure SGLT-2 Inhibitors and Cardiovascular Risk: An Analysis of CVD-REAL Fluid Volume Overload and Congestion in Heart Failure: Time to Reconsider Pathophysiology and How Volume Is Assessed Novel percutaneous interventional therapies in heart failure with preserved ejection fraction: an integrative review Economic and Quality-of-Life Outcomes of Natriuretic Peptide–Guided Therapy for Heart Failure 2019 ACC Expert Consensus Decision Pathway on Risk Assessment, Management, and Clinical Trajectory of Patients Hospitalized With Heart Failure: A Report of the American College of Cardiology Solution Set Oversight Committee The spectrum of heart failure: value of left ventricular ejection fraction and its moving trajectories
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Review Article2020 Jul 16;229:1-17.

JOURNAL：Am Heart J . Article Link

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure

CR Olsen, RJ Mentz, KJ Anstrom et al. Keywords: machine learning; artificial intelligence;

ABSTRACT

Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.

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